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Record W4316467912 · doi:10.5539/ijel.v13n1p105

Foreign Language Influence: A Case Study of English on Shop Signs in Taif City of Saudi Arabia

2023· article· en· W4316467912 on OpenAlexvenueno aff
Jalal Almathkuri

Bibliographic record

VenueInternational Journal of English Linguistics · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsTransliterationLinguisticsArabicForeign languageEnglish languageBusinessPsychology

Abstract

fetched live from OpenAlex

This study investigates the presence of English as the business language in Taif, Saudi Arabia. It also explores the influence and familiarity of English in the business sector. Linguistic landscape of 162 photos of commercial shops and interview of seven shop owners were employed to analyze the language use of shop signs in the Taif market. The study results were classified into three major categories, bilingual, transliteration and monolingual signs. The research has revealed that English is used as a prestigious language that is necessary to attract customers. However, Arabic presence is inevitably needed because of the linguistic background of the customers. The study concluded that in relation to the extent of using English in shop signs, and the frequent use of English words, whether on the bilingual signs or the transliteration ones, there is a need of policy formulation and implementation for lexical assimilation of borrowed words of English.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.292
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicLinguistics, Language Diversity, and IdentityFrench-language works237,207